调水调沙期小浪底水库出库泥沙组分估算研究

Research on Sediment Composition Estimation During the Period of Water-Sediment Regulation in Xiaolangdi Reservoir

为实现小浪底水库出库泥沙组分的准确估算,基于2002—2019年调水调沙期水沙系列数据,分别采用XGBoost、KNN、GPR三种机器学习算法建立综合考虑各影响因素的出库泥沙各组分估算模型。实例分析结果表明,应用机器学习算法进行出库泥沙组分估算是有效的,各模型估算值与实际值之间相关性良好;针对不同出库泥沙组分,KNN算法建立的估算模型的评估指标决定系数最大、平均绝对误差和均方根误差最小,表明KNN算法模型在出库泥沙组分估算方面具有更高的准确性和精度。

In order to achieve the accurate sediment composition estimation of out reservoir in Xiaolangdi Reservoir,the estimation models for each sediment composition of out reservoir that comprehensively considering various influencing factors were established,using three different machine learning algorithms including XGBoost,KNN and GPR and based on the series data of water and sediment during the period of water-sediment regulation from 2002 to 2019.The analysis results show that it is effective to apply machine learning algorithms to estimate the sediment composition of out reservoir,and there is the good correlation between the estimated and actual value of each model.In comparison,for different sediment compositions of out reservoir,the coefficient of determination R^(2) of the estimation model using KNN algorithm is the highest,and the average absolute error E_(MAE) and root mean square error E_(RMSE) of it are all the smallest,which shows that the KNN algorithm model has higher accuracy and precision on the sediment composition estimation of out reservoir.